My name is Oliver Parson, and I'm currently employed as a Data Scientist at Centrica Connected Home. I'm interested in investigating the ways in which machine learning can be used to break down household energy consumption data into individual appliances, also known as Non-intrusive Appliance Load Monitoring (NIALM) or energy disaggregation.

Sunday, 16 June 2013

AMPds data set released

Stephen Makonin recently released the first version of the Almanac of Minutely Power Data set. The data set contains 1 minute aggregate meter readings as well as sub-metered readings from 19 individual circuits. Each reading includes measurements of voltage, current, frequency, power factor, real power, reactive power and apparent power. Furthermore, the aggregate gas and water consumption was also measured at 1 minute intervals, in addition to 1 individual usage for each utility. The data set spans an entire year from April 2012 to March 2013 from a single household in the greater Vancouver area, BC, Canada. The data set is available to anyone for free, although the authors require a username and password to be requested for the purposes of usage tracking.

The authors of the data set have described collection process in more detail in the accompanying paper, as well as showing benchmark results of a method based on independent time slice combinatorial optimisation: